Bibliometric Analysis on Security of Different Layers in Internet of Things (IoT) Environment

2021 International Conference on Emerging Smart Computing and Informatics (ESCI), Journal Year: 2023, Volume and Issue: unknown, P. 1 - 6

Published: March 1, 2023

The Internet of things (IoT), an integral part the daily lives urban habitats, is gaining pace in attaining importance all aspects. In turn, this gives intelligence to make people's more comfortable and improves their quality life. association components with internet for establishing communication among themselves facilitates a vast domain varied applications different industries. This makes way several security issues that are associated cloud or internet. However, number flaws have been found will keep Things as technology at risk. These often go unseen ignored until threat caused. threats IoT extremely hazardous complex. Thus, protocols used application layer utmost importance. A comprehensive detailed study IoT, its architecture, components, threats, bibliometric analysis carried out study.

Language: Английский

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Security Enhancement in Surveillance Cloud Using Machine Learning Techniques

International Journal on Recent and Innovation Trends in Computing and Communication, Journal Year: 2023, Volume and Issue: 11(3s), P. 46 - 55

Published: March 11, 2023

Most industries are now switching from traditional modes to cloud environments and cloud-based services. It is essential create a secure environment for the space in order provide consumers with safe protected transactions. Here, we discuss suggested approaches creating reliable surveillance cloud. When assessing security of vital locations, data crucial. We implementing machine learning methods improve more precisely classify image pixels, make use Support Vector Machines (SVM) Fuzzy C-means Clustering (FCM). also extend conventional two-tiered design by adding third level, CloudSec module, lower risk potential disclosure data.In our work evaluates how well proposed model (FCM-SVM) performed against contemporary models like ANN, KNN, SVD, Naive Bayes. Comparing other cutting-edge models, found that it better, an average accuracy 94.4%.

Language: Английский

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An Innovative Non-Invasive Approach to Personalized Nutrition and Metabolism Monitoring Using Wearable Sensors

Published: Dec. 18, 2023

This study introduces an innovative approach to nutrition and body metabolism monitoring using spectroscopy EMG sensors with cloud-based machine learning. The methodology begins the setup of for data collection, followed by robust preprocessing techniques, including noise filtering, normalization, imputation. By implementing learning algorithms, encompassing regression classification models, are evaluated their suitability in predicting content metabolism. system implemented from starting collection a diverse dataset employing feature extraction techniques on data. successful prediction parameters is demonstrated, indicating system's potential transform how individuals manage health.The study's tangible outcomes presented through display parameters, showcasing real-time values carbohydrates, protein, fat obtained sensor stored cloud. non-invasive method sensing holds promise revolutionizing individual health management, offering actionable insights into dietary choices metabolic health. results contribute advancement technology-driven healthcare solutions, paving way improved personalized lifestyle management.

Language: Английский

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Machine Learning for predicting climate change in the environment: Review

Salud Ciencia y Tecnología - Serie de Conferencias, Journal Year: 2023, Volume and Issue: 2, P. 465 - 465

Published: Oct. 10, 2023

Climate changes currently occur abruptly and immediately being unpredictable by the population, causing damage material losses, but with support of current technologies, such as artificial intelligence: machine learning, will help us to anticipate these events. Therefore, this review aims analyze effectiveness learning for prediction climate in environment, provide validity its performance improvement. The methodology employed systematic consisted using PICO establish eligibility criteria grouping them into components that were finally reduced PIOC, which following question was established, what extent does Machine Learning improve environment? gave way development keywords creation search equation. Subsequently, PRISMA used discard articles exclusion inclusion, starting a base 2020 after applying all filters, 22 included SLR. results showed superior unraveling complex interactive associations between environment plant diversity, furthermore ELM method generally provided accuracy other methods predicting monthly soil temperatures at various depths. It concluded is an effective stands out among types intelligence showing positive relationship predict temperature according approach presented, most model suits research should be applied obtain better results.

Language: Английский

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Anomaly detection framework for IoT-enabled appliances using machine learning

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(7), P. 9811 - 9835

Published: April 30, 2024

Language: Английский

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